As recently proposed in our previous work, the two-dimensional CT fluoroscopy image series can be used to track the three-dimensional motion of a pulmonary lesion. The assumption is that the lung tissue is locally rigid, so that the real-time CT fluoroscopy image can be combined with a preoperative CT volume to infer the position of the lesion when the lesion is not in the CT fluoroscopy imaging plane. In this paper, we validate the basic properties of our tracking algorithm using a synthetic four-dimensional lung dataset. The motion tracking result is compared to the ground truth of the four-dimensional dataset. The optimal parameter configurations of the algorithm are discussed. The robustness and accuracy of the tracking algorithm are presented. The error analysis shows that the local rigidity error is the principle component of the tracking error. The error increases as the lesion moves away from the image region being registered. Using the synthetic four-dimensional lung data, the average tracking error over a complete respiratory cycle is 0.8 mm for target lesions inside the lung. As a result, the motion tracking algorithm can potentially alleviate the effect of respiratory motion in CT fluoroscopy-guided lung biopsy.